Overview

Dataset statistics

Number of variables14
Number of observations9578
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory112.0 B

Variable types

Categorical3
Numeric11

Warnings

credit_policy is highly correlated with inq_last_6mthsHigh correlation
int_rate is highly correlated with ficoHigh correlation
fico is highly correlated with int_rate and 1 other fieldsHigh correlation
revol_util is highly correlated with ficoHigh correlation
inq_last_6mths is highly correlated with credit_policyHigh correlation
int_rate is highly correlated with ficoHigh correlation
fico is highly correlated with int_rate and 1 other fieldsHigh correlation
revol_bal is highly correlated with revol_utilHigh correlation
revol_util is highly correlated with fico and 1 other fieldsHigh correlation
int_rate is highly correlated with ficoHigh correlation
fico is highly correlated with int_rateHigh correlation
inq_last_6mths is highly correlated with credit_policyHigh correlation
credit_policy is highly correlated with inq_last_6mths and 1 other fieldsHigh correlation
fico is highly correlated with credit_policy and 2 other fieldsHigh correlation
int_rate is highly correlated with fico and 1 other fieldsHigh correlation
revol_util is highly correlated with fico and 1 other fieldsHigh correlation
revol_bal has 321 (3.4%) zeros Zeros
revol_util has 297 (3.1%) zeros Zeros
inq_last_6mths has 3637 (38.0%) zeros Zeros
delinq_2yrs has 8458 (88.3%) zeros Zeros
pub_rec has 9019 (94.2%) zeros Zeros

Reproduction

Analysis started2021-11-19 07:40:32.582965
Analysis finished2021-11-19 07:41:13.915581
Duration41.33 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

credit_policy
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
1
7710 
0
1868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9578
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Length

2021-11-19T13:11:14.369939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-19T13:11:14.545074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring characters

ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9578
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring scripts

ValueCountFrequency (%)
Common9578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII9578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17710
80.5%
01868
 
19.5%

purpose
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
debt_consolidation
3957 
all_other
2331 
credit_card
1262 
home_improvement
629 
small_business
619 
Other values (2)
780 

Length

Max length18
Median length14
Mean length14.06431405
Min length9

Characters and Unicode

Total characters134708
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowcredit_card
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowcredit_card

Common Values

ValueCountFrequency (%)
debt_consolidation3957
41.3%
all_other2331
24.3%
credit_card1262
 
13.2%
home_improvement629
 
6.6%
small_business619
 
6.5%
major_purchase437
 
4.6%
educational343
 
3.6%

Length

2021-11-19T13:11:15.041782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-19T13:11:15.240215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
debt_consolidation3957
41.3%
all_other2331
24.3%
credit_card1262
 
13.2%
home_improvement629
 
6.6%
small_business619
 
6.5%
major_purchase437
 
4.6%
educational343
 
3.6%

Most occurring characters

ValueCountFrequency (%)
o16240
12.1%
t12479
9.3%
e10836
 
8.0%
d10781
 
8.0%
i10767
 
8.0%
l10200
 
7.6%
a9729
 
7.2%
n9505
 
7.1%
_9235
 
6.9%
c7261
 
5.4%
Other values (9)27675
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter125473
93.1%
Connector Punctuation9235
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o16240
12.9%
t12479
9.9%
e10836
8.6%
d10781
8.6%
i10767
8.6%
l10200
8.1%
a9729
7.8%
n9505
7.6%
c7261
 
5.8%
s6870
 
5.5%
Other values (8)20805
16.6%
Connector Punctuation
ValueCountFrequency (%)
_9235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin125473
93.1%
Common9235
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o16240
12.9%
t12479
9.9%
e10836
8.6%
d10781
8.6%
i10767
8.6%
l10200
8.1%
a9729
7.8%
n9505
7.6%
c7261
 
5.8%
s6870
 
5.5%
Other values (8)20805
16.6%
Common
ValueCountFrequency (%)
_9235
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII134708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o16240
12.1%
t12479
9.3%
e10836
 
8.0%
d10781
 
8.0%
i10767
 
8.0%
l10200
 
7.6%
a9729
 
7.2%
n9505
 
7.1%
_9235
 
6.9%
c7261
 
5.4%
Other values (9)27675
20.5%

int_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct249
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1226400606
Minimum0.06
Maximum0.2164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:15.564381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.0774
Q10.1039
median0.1221
Q30.1407
95-th percentile0.167
Maximum0.2164
Range0.1564
Interquartile range (IQR)0.0368

Descriptive statistics

Standard deviation0.02684698721
Coefficient of variation (CV)0.2189087896
Kurtosis-0.2243235112
Mean0.1226400606
Median Absolute Deviation (MAD)0.0186
Skewness0.1644199135
Sum1174.6465
Variance0.0007207607224
MonotonicityNot monotonic
2021-11-19T13:11:15.796726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1253354
 
3.7%
0.0894299
 
3.1%
0.1183243
 
2.5%
0.1218215
 
2.2%
0.0963210
 
2.2%
0.1114206
 
2.2%
0.08198
 
2.1%
0.1287197
 
2.1%
0.1148193
 
2.0%
0.0859187
 
2.0%
Other values (239)7276
76.0%
ValueCountFrequency (%)
0.068
 
0.1%
0.06394
 
< 0.1%
0.06769
 
0.1%
0.070523
 
0.2%
0.07129
 
0.1%
0.071428
 
0.3%
0.073732
0.3%
0.07472
0.8%
0.074333
0.3%
0.075138
0.4%
ValueCountFrequency (%)
0.21642
 
< 0.1%
0.21217
0.1%
0.2092
 
< 0.1%
0.20866
0.1%
0.20524
< 0.1%
0.20176
0.1%
0.20161
 
< 0.1%
0.20119
0.1%
0.19828
0.1%
0.19796
0.1%

installment
Real number (ℝ≥0)

Distinct4788
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean319.0894132
Minimum15.67
Maximum940.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:16.006166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile65.5595
Q1163.77
median268.95
Q3432.7625
95-th percentile756.2655
Maximum940.14
Range924.47
Interquartile range (IQR)268.9925

Descriptive statistics

Standard deviation207.0713015
Coefficient of variation (CV)0.6489444429
Kurtosis0.1379077383
Mean319.0894132
Median Absolute Deviation (MAD)124.7
Skewness0.9125224624
Sum3056238.4
Variance42878.5239
MonotonicityNot monotonic
2021-11-19T13:11:16.201642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317.7241
 
0.4%
316.1134
 
0.4%
319.4729
 
0.3%
381.2627
 
0.3%
662.6827
 
0.3%
156.124
 
0.3%
320.9524
 
0.3%
334.6723
 
0.2%
669.3323
 
0.2%
188.0223
 
0.2%
Other values (4778)9303
97.1%
ValueCountFrequency (%)
15.671
< 0.1%
15.691
< 0.1%
15.751
< 0.1%
15.761
< 0.1%
15.911
< 0.1%
16.081
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
16.731
< 0.1%
ValueCountFrequency (%)
940.141
 
< 0.1%
926.832
< 0.1%
922.421
 
< 0.1%
918.022
< 0.1%
916.952
< 0.1%
914.422
< 0.1%
913.633
< 0.1%
910.441
 
< 0.1%
909.251
 
< 0.1%
907.62
< 0.1%

log_annual_inc
Real number (ℝ≥0)

Distinct1987
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.93211714
Minimum7.547501683
Maximum14.52835448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:16.412113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.547501683
5-th percentile9.917893268
Q110.55841352
median10.92888357
Q311.29129292
95-th percentile11.91839057
Maximum14.52835448
Range6.980852797
Interquartile range (IQR)0.7328793975

Descriptive statistics

Standard deviation0.6148127514
Coefficient of variation (CV)0.05623912949
Kurtosis1.609004138
Mean10.93211714
Median Absolute Deviation (MAD)0.366945765
Skewness0.02866810657
Sum104707.8179
Variance0.3779947192
MonotonicityNot monotonic
2021-11-19T13:11:16.751426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.00209984308
 
3.2%
10.81977828248
 
2.6%
10.30895266224
 
2.3%
10.59663473224
 
2.3%
10.71441777221
 
2.3%
11.22524339196
 
2.0%
11.15625052165
 
1.7%
10.77895629149
 
1.6%
10.91508846147
 
1.5%
11.08214255146
 
1.5%
Other values (1977)7550
78.8%
ValueCountFrequency (%)
7.5475016831
 
< 0.1%
7.600902461
 
< 0.1%
8.1016777471
 
< 0.1%
8.1605182471
 
< 0.1%
8.1886891241
 
< 0.1%
8.294049643
< 0.1%
8.3428398041
 
< 0.1%
8.4118326761
 
< 0.1%
8.4763711972
< 0.1%
8.4945385011
 
< 0.1%
ValueCountFrequency (%)
14.528354481
 
< 0.1%
14.180153671
 
< 0.1%
14.124464771
 
< 0.1%
13.997832111
 
< 0.1%
13.710150042
< 0.1%
13.56704922
< 0.1%
13.543701831
 
< 0.1%
13.487006491
 
< 0.1%
13.470199371
 
< 0.1%
13.458835613
< 0.1%

dti
Real number (ℝ≥0)

Distinct2529
Distinct (%)26.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.60667885
Minimum0
Maximum29.96
Zeros89
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:17.099275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.27
Q17.2125
median12.665
Q317.95
95-th percentile23.65
Maximum29.96
Range29.96
Interquartile range (IQR)10.7375

Descriptive statistics

Standard deviation6.883969541
Coefficient of variation (CV)0.5460573418
Kurtosis-0.9003553617
Mean12.60667885
Median Absolute Deviation (MAD)5.385
Skewness0.02394102295
Sum120746.77
Variance47.38903664
MonotonicityNot monotonic
2021-11-19T13:11:17.433349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
089
 
0.9%
1019
 
0.2%
0.616
 
0.2%
15.113
 
0.1%
1213
 
0.1%
13.1613
 
0.1%
613
 
0.1%
19.213
 
0.1%
10.812
 
0.1%
15.612
 
0.1%
Other values (2519)9365
97.8%
ValueCountFrequency (%)
089
0.9%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.042
 
< 0.1%
0.051
 
< 0.1%
0.073
 
< 0.1%
0.082
 
< 0.1%
0.092
 
< 0.1%
0.122
 
< 0.1%
ValueCountFrequency (%)
29.961
< 0.1%
29.951
< 0.1%
29.91
< 0.1%
29.741
< 0.1%
29.721
< 0.1%
29.72
< 0.1%
29.61
< 0.1%
29.471
< 0.1%
29.421
< 0.1%
29.411
< 0.1%

fico
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean710.8463145
Minimum612
Maximum827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:18.268116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum612
5-th percentile657
Q1682
median707
Q3737
95-th percentile782
Maximum827
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation37.97053723
Coefficient of variation (CV)0.05341595849
Kurtosis-0.4223123103
Mean710.8463145
Median Absolute Deviation (MAD)25
Skewness0.4712597399
Sum6808486
Variance1441.761697
MonotonicityNot monotonic
2021-11-19T13:11:18.574297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
687548
 
5.7%
682536
 
5.6%
692498
 
5.2%
697476
 
5.0%
702472
 
4.9%
707444
 
4.6%
667438
 
4.6%
677427
 
4.5%
717424
 
4.4%
662414
 
4.3%
Other values (34)4901
51.2%
ValueCountFrequency (%)
6122
 
< 0.1%
6171
 
< 0.1%
6221
 
< 0.1%
6272
 
< 0.1%
6326
 
0.1%
6375
 
0.1%
642102
1.1%
647112
1.2%
652131
1.4%
657127
1.3%
ValueCountFrequency (%)
8271
 
< 0.1%
8225
 
0.1%
8176
 
0.1%
81233
 
0.3%
80745
 
0.5%
80255
0.6%
79776
0.8%
79297
1.0%
78785
0.9%
782118
1.2%

days_with_cr_line
Real number (ℝ≥0)

Distinct2687
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4560.767197
Minimum178.9583333
Maximum17639.95833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:18.898430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum178.9583333
5-th percentile1320.041667
Q12820
median4139.958333
Q35730
95-th percentile9329.958333
Maximum17639.95833
Range17461
Interquartile range (IQR)2910

Descriptive statistics

Standard deviation2496.930377
Coefficient of variation (CV)0.5474803403
Kurtosis1.937860594
Mean4560.767197
Median Absolute Deviation (MAD)1440.083334
Skewness1.155748227
Sum43683028.21
Variance6234661.307
MonotonicityNot monotonic
2021-11-19T13:11:19.246499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
366050
 
0.5%
363048
 
0.5%
399046
 
0.5%
441044
 
0.5%
360041
 
0.4%
255038
 
0.4%
408038
 
0.4%
180037
 
0.4%
369037
 
0.4%
402035
 
0.4%
Other values (2677)9164
95.7%
ValueCountFrequency (%)
178.95833331
 
< 0.1%
180.04166673
< 0.1%
1811
 
< 0.1%
183.04166671
 
< 0.1%
209.04166671
 
< 0.1%
2101
 
< 0.1%
212.04166671
 
< 0.1%
238.95833335
0.1%
240.04166671
 
< 0.1%
291.95833331
 
< 0.1%
ValueCountFrequency (%)
17639.958331
< 0.1%
176161
< 0.1%
166521
< 0.1%
163501
< 0.1%
162601
< 0.1%
16259.041671
< 0.1%
162131
< 0.1%
159901
< 0.1%
156921
< 0.1%
15420.958331
< 0.1%

revol_bal
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7869
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16913.96388
Minimum0
Maximum1207359
Zeros321
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:19.605539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile127.7
Q13187
median8596
Q318249.5
95-th percentile57654.3
Maximum1207359
Range1207359
Interquartile range (IQR)15062.5

Descriptive statistics

Standard deviation33756.18956
Coefficient of variation (CV)1.995758641
Kurtosis259.655203
Mean16913.96388
Median Absolute Deviation (MAD)6488
Skewness11.16105849
Sum162001946
Variance1139480333
MonotonicityNot monotonic
2021-11-19T13:11:19.983595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0321
 
3.4%
29810
 
0.1%
25510
 
0.1%
6829
 
0.1%
3468
 
0.1%
22296
 
0.1%
1826
 
0.1%
10856
 
0.1%
80355
 
0.1%
15
 
0.1%
Other values (7859)9192
96.0%
ValueCountFrequency (%)
0321
3.4%
15
 
0.1%
22
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
54
 
< 0.1%
65
 
0.1%
71
 
< 0.1%
94
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
12073591
< 0.1%
9520131
< 0.1%
6025191
< 0.1%
5089611
< 0.1%
4077941
< 0.1%
4019411
< 0.1%
3941071
< 0.1%
3888921
< 0.1%
3854891
< 0.1%
3744871
< 0.1%

revol_util
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1035
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.79923575
Minimum0
Maximum119
Zeros297
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:20.342602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q122.6
median46.3
Q370.9
95-th percentile94
Maximum119
Range119
Interquartile range (IQR)48.3

Descriptive statistics

Standard deviation29.01441697
Coefficient of variation (CV)0.6199762988
Kurtosis-1.116466996
Mean46.79923575
Median Absolute Deviation (MAD)24.2
Skewness0.05998544258
Sum448243.08
Variance841.8363919
MonotonicityNot monotonic
2021-11-19T13:11:20.690671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0297
 
3.1%
0.526
 
0.3%
47.822
 
0.2%
0.322
 
0.2%
73.722
 
0.2%
3.321
 
0.2%
0.121
 
0.2%
0.720
 
0.2%
120
 
0.2%
0.220
 
0.2%
Other values (1025)9087
94.9%
ValueCountFrequency (%)
0297
3.1%
0.041
 
< 0.1%
0.121
 
0.2%
0.220
 
0.2%
0.322
 
0.2%
0.412
 
0.1%
0.526
 
0.3%
0.612
 
0.1%
0.720
 
0.2%
0.814
 
0.1%
ValueCountFrequency (%)
1191
< 0.1%
108.81
< 0.1%
106.51
< 0.1%
106.41
< 0.1%
106.21
< 0.1%
106.11
< 0.1%
105.71
< 0.1%
105.31
< 0.1%
105.21
< 0.1%
104.31
< 0.1%

inq_last_6mths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5774692
Minimum0
Maximum33
Zeros3637
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:21.007855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.200245315
Coefficient of variation (CV)1.394794469
Kurtosis26.28813144
Mean1.5774692
Median Absolute Deviation (MAD)1
Skewness3.584150856
Sum15109
Variance4.841079446
MonotonicityNot monotonic
2021-11-19T13:11:21.285081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
03637
38.0%
12462
25.7%
21384
 
14.4%
3864
 
9.0%
4475
 
5.0%
5278
 
2.9%
6165
 
1.7%
7100
 
1.0%
872
 
0.8%
947
 
0.5%
Other values (18)94
 
1.0%
ValueCountFrequency (%)
03637
38.0%
12462
25.7%
21384
 
14.4%
3864
 
9.0%
4475
 
5.0%
5278
 
2.9%
6165
 
1.7%
7100
 
1.0%
872
 
0.8%
947
 
0.5%
ValueCountFrequency (%)
331
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
242
< 0.1%
201
 
< 0.1%
192
< 0.1%
184
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1637084986
Minimum0
Maximum13
Zeros8458
Zeros (%)88.3%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:21.465633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5462149246
Coefficient of variation (CV)3.336509278
Kurtosis71.43318185
Mean0.1637084986
Median Absolute Deviation (MAD)0
Skewness6.061793276
Sum1568
Variance0.2983507439
MonotonicityNot monotonic
2021-11-19T13:11:21.723940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
08458
88.3%
1832
 
8.7%
2192
 
2.0%
365
 
0.7%
419
 
0.2%
56
 
0.1%
62
 
< 0.1%
131
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
08458
88.3%
1832
 
8.7%
2192
 
2.0%
365
 
0.7%
419
 
0.2%
56
 
0.1%
62
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
111
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
62
 
< 0.1%
56
 
0.1%
419
 
0.2%
365
 
0.7%
2192
 
2.0%
1832
8.7%

pub_rec
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0621215285
Minimum0
Maximum5
Zeros9019
Zeros (%)94.2%
Negative0
Negative (%)0.0%
Memory size75.0 KiB
2021-11-19T13:11:21.954435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2621263264
Coefficient of variation (CV)4.219573032
Kurtosis38.7810072
Mean0.0621215285
Median Absolute Deviation (MAD)0
Skewness5.12643446
Sum595
Variance0.06871021098
MonotonicityNot monotonic
2021-11-19T13:11:22.185705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
09019
94.2%
1533
 
5.6%
219
 
0.2%
35
 
0.1%
41
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
09019
94.2%
1533
 
5.6%
219
 
0.2%
35
 
0.1%
41
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
219
 
0.2%
1533
 
5.6%
09019
94.2%

not_fully_paid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.0 KiB
0
8045 
1
1533 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9578
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Length

2021-11-19T13:11:22.701325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-19T13:11:22.868877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring characters

ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9578
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common9578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08045
84.0%
11533
 
16.0%

Interactions

2021-11-19T13:10:38.374749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:38.801427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:39.132508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:39.468666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:39.784916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:40.094087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:40.425235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:40.744349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:41.060502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:41.372701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:41.684833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:41.989019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:42.280240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:42.536553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:42.808860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:43.077141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:43.330464image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:43.616663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:43.800173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:43.971714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:44.128295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:44.277895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:44.448473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:44.762600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:45.041886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:45.335069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:45.625326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:45.903548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:46.210726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:46.505937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:46.797184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:47.090373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:47.379600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:47.660880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:47.962075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:48.234347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:48.520548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:48.801795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:49.070078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:49.368281image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:49.656509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:49.940782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:50.224024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:50.507260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:50.783581image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:51.066737image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:51.322082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:51.587371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:51.844687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:52.092029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:52.368256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:52.644717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:52.907013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:53.562265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:53.821564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:54.075891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:54.395093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:54.677338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:54.982490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:55.284750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:55.562007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:55.878162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:56.185341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:56.491488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:56.793713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:57.093910image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:57.391212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:57.700357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:57.986624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:58.290778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:58.584026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:58.862282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:59.173449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:59.474641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:59.727932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:10:59.904460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:00.080987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:00.248540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:00.456984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:00.735273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:01.024498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:01.308705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:01.581974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:01.887191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:02.179409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:02.464646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:02.756831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:03.044063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:03.324346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:03.629535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:03.909748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:04.201992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:04.489230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:04.760504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:05.062699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:05.362971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:05.648208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:05.937429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:06.222672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:06.504919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:06.816085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:07.093343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:07.383567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:07.664816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:07.934094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:08.234294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:08.527709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:08.814739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:09.100973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:09.383218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:09.663469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:09.958679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:10.227958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:10.507179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:10.782475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:11.043777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:11.334000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:11.620202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:11.900452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:12.176745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-19T13:11:12.452973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-19T13:11:23.063498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-19T13:11:23.557168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-19T13:11:24.041872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-19T13:11:24.530540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-19T13:11:24.954486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-19T13:11:12.980596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-19T13:11:13.632850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

credit_policypurposeint_rateinstallmentlog_annual_incdtificodays_with_cr_linerevol_balrevol_utilinq_last_6mthsdelinq_2yrspub_recnot_fully_paid
01debt_consolidation0.1189829.1011.35040719.487375639.9583332885452.10000
11credit_card0.1071228.2211.08214314.297072760.0000003362376.70000
21debt_consolidation0.1357366.8610.37349111.636824710.000000351125.61000
31debt_consolidation0.1008162.3411.3504078.107122699.9583333366773.21000
41credit_card0.1426102.9211.29973214.976674066.000000474039.50100
51credit_card0.0788125.1311.90496816.987276120.0416675080751.00000
61debt_consolidation0.1496194.0210.7144184.006673180.041667383976.80011
71all_other0.1114131.2211.00210011.087225116.0000002422068.60001
81home_improvement0.113487.1911.40756517.256823989.0000006990951.11000
91debt_consolidation0.122184.1210.20359210.007072730.041667563023.01000

Last rows

credit_policypurposeint_rateinstallmentlog_annual_incdtificodays_with_cr_linerevol_balrevol_utilinq_last_6mthsdelinq_2yrspub_recnot_fully_paid
95680all_other0.197937.0610.64542522.176675916.0000002885459.86010
95690home_improvement0.1426823.3412.4292163.627223239.9583333357583.95001
95700all_other0.1671113.6310.64542528.066723210.0416672575963.85001
95710all_other0.1568161.0111.2252438.006777230.000000690929.24011
95720debt_consolidation0.156569.9810.1104727.026628190.041667299939.56001
95730all_other0.1461344.7612.18075510.3967210474.00000021537282.12001
95740all_other0.1253257.7011.1418620.217224380.0000001841.15001
95750debt_consolidation0.107197.8110.59663513.096873450.0416671003682.98001
95760home_improvement0.1600351.5810.81977819.186921800.00000003.25001
95770debt_consolidation0.1392853.4311.26446416.287324740.0000003787957.06001